'''simple docstring'''





import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import DecisionTransformerModel
    from transformers.models.decision_transformer.modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
    )




class 			__A :



    def __init__(self       :       Optional[Any]  ,     __a       :       str  ,     __a       :       Union[str, Any]=13  ,     __a       :       List[Any]=7  ,     __a       :       List[str]=6  ,     __a       :       Union[str, Any]=17  ,     __a       :       List[Any]=23  ,     __a       :       Optional[int]=11  ,     __a       :       str=True  ,     ):
         UpperCAmelCase_			       =  parent
         UpperCAmelCase_			       =  batch_size
         UpperCAmelCase_			       =  seq_length
         UpperCAmelCase_			       =  act_dim
         UpperCAmelCase_			       =  state_dim
         UpperCAmelCase_			       =  hidden_size
         UpperCAmelCase_			       =  max_length
         UpperCAmelCase_			       =  is_training



    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  floats_tensor((self.batch_size, self.seq_length, self.state_dim)					)
         UpperCAmelCase_			       =  floats_tensor((self.batch_size, self.seq_length, self.act_dim)					)
         UpperCAmelCase_			       =  floats_tensor((self.batch_size, self.seq_length, 1)					)
         UpperCAmelCase_			       =  floats_tensor((self.batch_size, self.seq_length, 1)					)
         UpperCAmelCase_			       =  ids_tensor((self.batch_size, self.seq_length)  ,     vocab_size=1000					)
         UpperCAmelCase_			       =  random_attention_mask((self.batch_size, self.seq_length)					)

         UpperCAmelCase_			       =  self.get_config()

         return (
             config,
             states,
             actions,
             rewards,
             returns_to_go,
             timesteps,
             attention_mask,
         )



    def    _lowercase		(self       :       Any					):
         return DecisionTransformerConfig(
             batch_size=self.batch_size  ,     seq_length=self.seq_length  ,     act_dim=self.act_dim  ,     state_dim=self.state_dim  ,     hidden_size=self.hidden_size  ,     max_length=self.max_length  ,     )



    def    _lowercase		(self       :       Tuple  ,     __a       :       Any  ,     __a       :       str  ,     __a       :       Optional[Any]  ,     __a       :       Optional[int]  ,     __a       :       Dict  ,     __a       :       List[str]  ,     __a       :       Optional[int]  ,     ):
         UpperCAmelCase_			       =  DecisionTransformerModel(config=__a					)
         model.to(__a					)
         model.eval()
         UpperCAmelCase_			       =  model(__a  ,     __a  ,     __a  ,     __a  ,     __a  ,     __a					)

         self.parent.assertEqual(result.state_preds.shape  ,     states.shape					)
         self.parent.assertEqual(result.action_preds.shape  ,     actions.shape					)
         self.parent.assertEqual(result.return_preds.shape  ,     returns_to_go.shape					)
         self.parent.assertEqual(
             result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length * 3, self.hidden_size)					)  # seq length *3 as there are 3 modelities: states, returns and actions






    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					(
             UpperCAmelCase_
         )       ,					
         )			       =  config_and_inputs
         UpperCAmelCase_			       =  {
             "states": states,
             "actions": actions,
             "rewards": rewards,
             "returns_to_go": returns_to_go,
             "timesteps": timesteps,
             "attention_mask": attention_mask,
         }
         return config, inputs_dict




@require_torch
class 			__A (					UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		str			  =    (DecisionTransformerModel,) if is_torch_available() else ()
    a__			:		List[str]			  =    ()
    a__			:		Optional[Any]			  =    {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

    # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
    a__			:		Any			  =    False

    # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
    a__			:		Dict			  =    False
    a__			:		List[str]			  =    False
    a__			:		List[str]			  =    False
    a__			:		List[Any]			  =    False
    a__			:		Optional[int]			  =    False
    a__			:		int			  =    False
    a__			:		Union[str, Any]			  =    False
    a__			:		List[str]			  =    False
    a__			:		List[Any]			  =    False



    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  DecisionTransformerModelTester(self					)
         UpperCAmelCase_			       =  ConfigTester(self  ,     config_class=__a  ,     hidden_size=37					)



    def    _lowercase		(self       :       Optional[int]					):
         self.config_tester.run_common_tests()



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a					)



    @slow
    def    _lowercase		(self       :       Optional[Any]					):
         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_			       =  DecisionTransformerModel.from_pretrained(__a					)
              self.assertIsNotNone(__a					)






    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_			       =  model_class(__a					)
              UpperCAmelCase_			       =  inspect.signature(model.forward					)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_			       =  [*signature.parameters.keys()]

              UpperCAmelCase_			       =  [
                  "states",
                  "actions",
                  "rewards",
                  "returns_to_go",
                  "timesteps",
                  "attention_mask",
              ]

              self.assertListEqual(arg_names[: len(__a					)]  ,     __a					)




@require_torch
class 			__A (					unittest.TestCase      ):



    @slow
    def    _lowercase		(self       :       List[Any]					):
         UpperCAmelCase_			       =  2  # number of steps of autoregressive prediction we will perform
         UpperCAmelCase_			       =  10  # defined by the RL environment, may be normalized
         UpperCAmelCase_			       =  DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"					)
         UpperCAmelCase_			       =  model.to(__a					)
         UpperCAmelCase_			       =  model.config
         torch.manual_seed(0					)
         UpperCAmelCase_			       =  torch.randn(1  ,     1  ,     config.state_dim					).to(device=__a  ,     dtype=torch.floataa					)  # env.reset()

         UpperCAmelCase_			       =  torch.tensor(
             [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]  ,     device=__a					)

         UpperCAmelCase_			       =  torch.tensor(__a  ,     device=__a  ,     dtype=torch.floataa					).reshape(1  ,     1  ,     1					)
         UpperCAmelCase_			       =  state
         UpperCAmelCase_			       =  torch.zeros(1  ,     0  ,     config.act_dim  ,     device=__a  ,     dtype=torch.floataa					)
         UpperCAmelCase_			       =  torch.zeros(1  ,     0  ,     device=__a  ,     dtype=torch.floataa					)
         UpperCAmelCase_			       =  torch.tensor(0  ,     device=__a  ,     dtype=torch.long					).reshape(1  ,     1					)

         for step in range(__a					):
              UpperCAmelCase_			       =  torch.cat([actions, torch.zeros(1  ,     1  ,     config.act_dim  ,     device=__a					)]  ,     dim=1					)
              UpperCAmelCase_			       =  torch.cat([rewards, torch.zeros(1  ,     1  ,     device=__a					)]  ,     dim=1					)

              UpperCAmelCase_			       =  torch.ones(1  ,     states.shape[1]					).to(dtype=torch.long  ,     device=states.device					)

              with torch.no_grad():
                   UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  model(
                       states=__a  ,     actions=__a  ,     rewards=__a  ,     returns_to_go=__a  ,     timesteps=__a  ,     attention_mask=__a  ,     return_dict=__a  ,     )

              self.assertEqual(action_pred.shape  ,     actions.shape					)
              self.assertTrue(torch.allclose(action_pred[0, -1]  ,     expected_outputs[step]  ,     atol=1E-4					)					)
              UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  (  # env.step(action)
                  torch.randn(1  ,     1  ,     config.state_dim					).to(device=__a  ,     dtype=torch.floataa					),
                  1.0,
                  False,
                  {},
              )

              UpperCAmelCase_			       =  action_pred[0, -1]
              UpperCAmelCase_			       =  torch.cat([states, state]  ,     dim=1					)
              UpperCAmelCase_			       =  returns_to_go[0, -1] - reward
              UpperCAmelCase_			       =  torch.cat([returns_to_go, pred_return.reshape(1  ,     1  ,     1					)]  ,     dim=1					)
              UpperCAmelCase_			       =  torch.cat(
                  [timesteps, torch.ones((1, 1)  ,     device=__a  ,     dtype=torch.long					) * (step + 1)]  ,     dim=1					)





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ = None )      ->str:




  '''simple docstring'''

  if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
    # old versions of hfh don't url-encode the file path
    _UpperCamelCase           =    quote(a__ )
  return hfh.hf_hub_url(a__    ,     a__    ,     repo_type="dataset"    ,     revision=a__ )




